Abstract
Medical image processing on edge devices is the key to local and efficient data processing. In the last decade, convolutional neural networks (CNNs) have dominated and achieved top performance in various medical imaging applications. However, CNNs are limited in their performance due to their inability to understand long-distance spatial relationships. The recently proposed vision transformer (ViT) learns long-distance spatial relationships of images based on self-attention, but these require large datasets for training. Hence, ViT-based architectures can be combined with CNNs to solve this problem. Yet, their use of edge devices has been barely explored. In this work, we investigate compact convolutional transformers (CCTs) and their ability to be deployed to edge devices. Using strategic design decisions, we were able to deploy CCT to Google Edge TPUs. In comparison to a reference CNN (ResNet50) that was also deployed to the Edge TPU, we reduce the model parameters by a factor of 35 and obtain a 7× inference time speed-up while obtaining competitive accuracy.
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© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Sun, Y., Kist, A.M. (2023). Compact Convolutional Transformers on Edge TPUs. In: Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2023. BVM 2023. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_32
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DOI: https://doi.org/10.1007/978-3-658-41657-7_32
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